Xu Min, Long Yu, Chen Peisheng, Li Ang, Xin Jian, Xu Yonghua
The Yancheng Clinical College of Xuzhou Medical University, Yancheng, China.
Department of General Surgery, The Affiliated Yancheng First Hospital of Nanjing University Medical School, Yancheng, China.
Front Oncol. 2025 Apr 8;15:1447055. doi: 10.3389/fonc.2025.1447055. eCollection 2025.
The study aims to establish a nomogram to predict advanced pancreatic carcinoma patients' overall survival (OS), incorporating albumin combined with systemic immune-inflammation index (A-SII) score and clinical characteristics.
A retrospective study analyzed the clinical data of 205 advanced pancreatic carcinoma patients without antitumor treatment from the Yancheng No.1 People's Hospital between October 2011 and June 2023, and the study divided patients into the training set and the validation set randomly at the proportion of three to one. The A-SII score was divided into scores of 0, 1, and 2 according to the different levels of albumin and SII. Receiver operating characteristic (ROC) curves and time-dependent area under the curve were used to evaluate the predictive ability of the A-SII score. The nomogram1 and nomogram2 were established by the multivariate Cox regression and Lasso Cox regression respectively. The study evaluated the discriminability of nomogram1 and nomogram2 based on C-index and ROC curves to obtain the optimal model. Subsequently, we plotted decision curve analyses (DCA) and calibration curves to estimate the clinical benefit and accuracy of nomogram2.
Lasso Cox regression showed that A-SII score, number of organ metastases, tumor size, chemotherapy, targeted therapy, Neutrophil-to-albumin ratio, and lactate dehydrogenase were independent prognostic factors for the OS of advanced pancreatic carcinoma patients. The C-index and ROC curve of the nomogram2 are better than the nomogram1. Subsequently, the DCA and calibration curve of the nomogram2 demonstrate excellent performance.
The nomogram based on the A-SII score and other independent prognostic factors determined by Lasso Cox regression can accurately predict the OS of patients suffering from advanced pancreatic carcinoma.
本研究旨在建立一种列线图，以预测晚期胰腺癌患者的总生存期（OS），该列线图纳入了白蛋白联合全身免疫炎症指数（A-SII）评分及临床特征。
一项回顾性研究分析了2011年10月至2023年6月期间盐城市第一人民医院205例未接受抗肿瘤治疗的晚期胰腺癌患者的临床资料，研究按三比一的比例将患者随机分为训练集和验证集。根据白蛋白和SII的不同水平，将A-SII评分分为0、1、2分。采用受试者工作特征（ROC）曲线及曲线下时间依赖性面积评估A-SII评分的预测能力。列线图1和列线图2分别通过多变量Cox回归和Lasso Cox回归建立。本研究基于C指数和ROC曲线评估列线图1和列线图2的区分能力，以获得最佳模型。随后，绘制决策曲线分析（DCA）和校准曲线，以评估列线图2的临床获益和准确性。
Lasso Cox回归显示，A-SII评分、器官转移数量、肿瘤大小、化疗、靶向治疗、中性粒细胞与白蛋白比值及乳酸脱氢酶是晚期胰腺癌患者OS的独立预后因素。列线图2的C指数和ROC曲线优于列线图1。随后，列线图2的DCA和校准曲线显示出优异的性能。
基于A-SII评分及Lasso Cox回归确定的其他独立预后因素构建的列线图，能够准确预测晚期胰腺癌患者的OS。
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